Ridge Objective

Evaluate the ridge-regression objective: the mean squared prediction error plus $\lambda \backslash lambda$ times the squared L2 norm of the weights. The intercept b is not penalised.

Implement solve(X, y, w, b, lam).

Returns. Return a Python float $\frac{1}{n}{\sum }_i(x_i^{\mathrm{\top }}w+b-y_i{)}^2+\lambda \mathrm{\parallel }w{\mathrm{\parallel }}_2^2\backslash ;\backslash frac1n\backslash sum\_i(x\_i^\backslash top\; w+b-y\_i)^2+\backslash lambda\backslash |w\backslash |\_2^2$.

Examples

Example 1

Input:

solve([[1.0, 0.0], [0.0, 1.0]], [1.0, 2.0], [1.0, 1.0], 0.0, 0.5)

Output:

1.5

Example 2

Input:

solve([[2.0], [4.0]], [1.0, 2.0], [0.5], 0.0, 10.0)

Output:

2.5